1. Field of the Invention
The present invention generally relates to methods and systems incorporating a neural network and a forward physical model for semiconductor applications.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on specimens to drive higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
Defect review typically involves re-detecting defects detected as such by an inspection process and generating additional information about the defects at a higher resolution using either a high magnification optical system or a scanning electron microscope (SEM). Defect review is therefore performed at discrete locations on specimens where defects have been detected by inspection. The higher resolution data for the defects generated by defect review is more suitable for determining attributes of the defects such as profile, roughness, more accurate size information, etc.
Metrology processes are also used at various steps during a semiconductor manufacturing process to monitor and control the process. Metrology processes are different than inspection processes in that, unlike inspection processes in which defects are detected on specimens, metrology processes are used to measure one or more characteristics of the specimens that cannot be determined using currently used inspection tools. For example, metrology processes are used to measure one or more characteristics of specimens such as a dimension (e.g., line width, thickness, etc.) of features formed on the specimens during a process such that the performance of the process can be determined from the one or more characteristics. In addition, if the one or more characteristics of the specimens are unacceptable (e.g., out of a predetermined range for the characteristic(s)), the measurements of the one or more characteristics of the specimens may be used to alter one or more parameters of the process such that additional specimens manufactured by the process have acceptable characteristic(s).
Metrology processes are also different than defect review processes in that, unlike defect review processes in which defects that are detected by inspection are re-visited in defect review, metrology processes may be performed at locations at which no defect has been detected. In other words, unlike defect review, the locations at which a metrology process is performed on specimens may be independent of the results of an inspection process performed on the specimens. In particular, the locations at which a metrology process is performed may be selected independently of inspection results.
As design rules shrink, the design that is formed on a specimen such as reticles and wafers, even when formed using an optimally performing process, can look much different from the actual design. For example, due to the inherent limitations of the physical processes involved in forming a design on a physical specimen, features in the design formed on the physical specimen typically have somewhat different characteristics than the design such as different shapes (e.g., due to corner rounding and other proximity effects) and can have somewhat different dimensions (e.g., due to proximity effects) even when the best possible version of the design has been formed on the specimen.
Sometimes, it is not possible to know how the design will appear on the specimen and in images of the specimen, on which the design information has been formed, generated by tools such as inspection tools, defect review tools, metrology tools and the like. However, it is often desirable to know how the design will appear on the specimen and in images generated by such tools for a number of reasons. One reason is to make sure that the design will be formed on the specimen in an acceptable manner. Another reason is to provide a reference for the design, which illustrates how the design is meant to be formed on the specimen, that can be used for one or more functions performed for the specimen. For example, in general, a reference is needed for defect detection so that any differences between the design formed on the specimen and the reference can be detected and identified as defects or potential defects.
Much work has therefore been done to develop various methods and systems that can simulate one image for a specimen from another image for the specimen. Conventional approaches in general involve two steps: (1) restoring or inversing the undesired optical effects (e.g., diffraction, interference, partial coherence, etc.); and (2) using the restored/processed imaging data as the input to train an application-specific neural network. Restoring or inversing the undesired optical effects can be performed through (a) either conventional image processing or signal processing algorithms (e.g., Lucy-Richardson deconvolution and regularized Lucy-Richardson deconvolution, Wiener filter, tool calibration, etc.); (b) first-principle optics simulation; or (c) supervised machine learning or deep learning algorithms, given that a training dataset can be obtained from tool measurements and/or through simulation.
There are, however, a number of disadvantages to the currently used methods. For example, currently used restoring/inversing algorithms (e.g., Lucy-Richardson deconvolution, Wiener filter) are often under-determinate and noise sensitive. In addition, the currently used restoring/inversing algorithms described above are computationally intensive (i.e., they are not suitable for real-time on-tool applications). The currently used restoring/inversing algorithms described above can also only be applied to algorithmically invertible optical parameters (e.g., it is still substantially difficult to perform phase retrieval on semiconductor optical images). Furthermore, the currently used restoring/inversing algorithms described above require the exact (at least good) estimated optical parameters as inputs. Moreover, currently used supervised training algorithms for restoring described above require a training dataset of tuples of original collected images and their corresponding restored images, which is often impossible or substantially expensive to be measured or simulated. In addition, the two-step approach is inefficient from the mathematically optimization perspective.
Accordingly, it would be advantageous to develop systems and methods for training a neural network that do not have one or more of the disadvantages described above.